: Biliary tract cancers (BTCs) including cholangiocarcinoma and gallbladder cancer are rare cancers associated with poor survival; most patients have advanced disease at diagnosis. Current ...chemotherapy reference regimens include cisplatin and gemcitabine as first-line; and oxaliplatin and 5-fluorouracil (FOLFOX) in second-line. Molecular profiling has identified several actionable therapeutic targets including isocitrate dehydrogenase (IDH)1 mutations. Ivosidenib is a reversible inhibitor of mutant IDH1; it is currently approved for the treatment of acute myeloid leukemia and has been studied in patients with advanced cholangiocarcinoma.
: This article introduces current treatments for BTC and sheds light on the mechanism of action, pharmacodynamics, pharmacokinetics, clinical efficacy, and safety of ivosidenib in advanced cholangiocarcinoma. The authors conclude with insights on the changing treatment paradigm created by emerging drugs and precision approaches.
: Ivosidenib is well tolerated, with good oral exposure and long half-life as shown by phase I data. In a phase III study, ivosidenib has demonstrated improved progression-free survival compared to placebo (median 2.7 vs 1.4 months; hazard ratio 0.37; 95% confidence interval 0.25-0.54; one-sided p < 0.0001); it has also demonstrated a trend toward increased overall survival in patients with cholangiocarcinoma and disease progression on prior chemotherapy. Final survival data from this study are pending presentation. Increased use of molecular profiling will continue to identify potential therapeutic targets and improve the prognosis of patients with these cancers.
Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely ...severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.
Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and ...increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET).
Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O
) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort.
The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801
0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.
CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.
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